The Calculus of Confidence: Modelling Vaccine Hesitancy and Strategies for Support (Preprint)
Bibliographic record
Abstract
BACKGROUND Vaccine hesitancy is a growing issue that the WHO ranks as one of the top 10 threats to global health. Public confidence in vaccines and rates of routine childhood vaccination have been declining around the world since the pandemic, when many countries saw the instatement of COVID-19 vaccine mandates. OBJECTIVE We leveraged the COVID-19 add-on study, conducted by the CHILD Cohort Study (Canada’s most phenotypically diverse, large prospective longitudinal birth cohort), to determine characteristics associated with adult participants’ vaccine hesitancy. Our goal was to identify potential strategies for addressing vaccine uptake concerns. This study complements others by examining more behavioural, socioeconomic, attitudinal and additional characteristics, in some cases with greater granularity, and by further exploring the effects of COVID-19 vaccine mandates on vaccine uptake and beliefs. METHODS We generated penalized logistic regression models and used statistical tests to analyze a dataset of nearly 700 questionnaire responses where vaccine hesitancy was measured by participants’ agreement or disagreement with the following statements: “Getting myself vaccinated is important for the health of others in my community” and “Getting vaccinated is a good way to protect myself from disease''. We also examined whether vaccination status or opinions changed after the imposition of vaccine mandates. RESULTS While vaccine mandates were successful in increasing COVID-19 specific vaccine uptake in hesitant individuals vs confident individuals, they were ineffective in modifying hesitant individuals’ beliefs about vaccines. Vaccine-confident individuals were more likely to engage in pandemic safety measures such as physical distancing, while vaccine-hesitant individuals were more likely to have or had chronic medical conditions in the past, experience economic precarity, have lower socioeconomic status and/or formal education level, have had difficulty accessing medical care, and rely on friends and internet sites that were not governmental for COVID-19 related information. CONCLUSIONS To ensure that relevant information regarding vaccines reaches all segments of the population, outreach strategies should be tailored to individuals with a variety of cultural or educational backgrounds. Improving access to medical care could also improve access to reliable information. Vaccine mandates do not impact an individual’s beliefs in vaccines, and so countering vaccine hesitancy itself is likely to be more effective in terms of ensuring continuous vaccine uptake in a population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".